Senior Data Scientist

SOM • Chicago, IL

6d • $140,000 - $160,000

About The Position

SOM is seeking a Senior Data Scientist to develop and implement statistical, predictive, optimization, machine learning, and AI solutions for business and design challenges. The role owns projects end to end, from problem discovery and data exploration through modeling, validation, deployment, and adoption, and applies methods appropriate to the problem and available data.

The Senior Data Scientist builds and evaluates models using rigorous analytical methods, prepares and validates data from multiple systems, and develops maintainable solutions intended for production use. The role translates analysis into applications, APIs, datasets, reports, dashboards, and other decision-support tools, communicating findings to technical and non-technical audiences. It also works with Data Engineering, DevOps, technology teams, and business stakeholders to support secure and sustainable deployment and integration.

The position contributes to data governance, documentation, shared engineering standards, and continuous improvement of SOM’s tools and practices. It also involves mentoring and knowledge sharing, developing knowledge of SOM’s business and architecture and design practices, and contributing to sustainable strategies, staff development, and standards, policies, and procedures.

Requirements

• At least 4 years of professional experience in Data Science, Machine Learning, Applied Statistics, Advanced Analytics, or a closely related field; demonstrated ownership and complexity of work are emphasized.
• Experience developing and evaluating statistical or machine learning models that solve real-world problems and support measurable business or operational outcomes.
• Experience taking analytical or machine learning work beyond experimentation into production or regular business use; familiarity with testing, reproducibility, deployment, monitoring, or related ML engineering practices.
• Strong proficiency in Python and SQL, and experience with common data science and machine learning libraries and large datasets.
• Experience with modern cloud data platforms or analytical environments such as Databricks, Snowflake, AWS, Azure, or comparable technologies.
• Strong grounding in statistical reasoning, model evaluation, feature engineering, experimentation, predictive modeling, and appropriate machine learning techniques.
• Ability to work independently, investigate unfamiliar problems, learn new tools and techniques, and make progress without detailed step-by-step direction.
• Ability to evaluate technical approaches and recognize when a simpler analytical or rules-based solution is more appropriate than machine learning.
• Clear and direct communication skills and ability to collaborate across technical teams and with non-technical leadership.
• Comfort working collaboratively, receiving feedback, following shared engineering standards, and progressively taking greater ownership.
• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field, or equivalent professional experience.

Nice To Haves

• Experience with Databricks, Spark/PySpark, MLflow, or similar machine learning and distributed data platforms.
• Familiarity with modern analytics engineering and development practices, including dbt, Git, automated testing, and CI/CD.
• Experience building analytical applications or internal data products using Streamlit or similar frameworks.
• Experience with optimization, forecasting, experimentation, causal inference, NLP, or generative AI.
• Experience working closely with Data Engineering or ML Engineering teams to productionize models.
• Experience with cloud platforms such as AWS or Azure.
• Interest in architecture, engineering, design, sustainability, or the built environment.

Responsibilities

• Develop and implement statistical, predictive, optimization, machine learning, and AI solutions for business and design challenges.
• Independently own data science projects from problem discovery and data exploration through modeling, validation, deployment, and adoption.
• Define project scope, identify risks and dependencies, break ambiguous problems into deliverables, and communicate progress and blockers.
• Build, validate, and evaluate predictive and statistical models; establish baselines, design experiments, select evaluation metrics, and communicate assumptions, limitations, and uncertainty.
• Develop production-ready analytical and machine learning solutions, including reproducibility, testing, versioning, deployment, monitoring, and maintainability.
• Mine, interrogate, and combine data from multiple systems; perform exploratory analysis, feature engineering, quality assurance, and validation.
• Translate analytical work into applications, APIs, datasets, reports, dashboards, and other decision-support tools.
• Communicate findings clearly to technical and non-technical audiences.
• Collaborate with Data Engineering, DevOps, technology teams, and business stakeholders on secure, sustainable deployment and integration.
• Build knowledge of SOM’s business, architecture and design practices, project delivery, and operational processes to identify valuable applications of data science.
• Follow security, governance, privacy, and documentation standards; document models, workflows, assumptions, dependencies, and production processes.
• Take responsibility for outcomes, proactively identify problems, recommend solutions, and follow work through implementation and adoption.
• Develop informed technical opinions while remaining open to feedback, alternative approaches, and new evidence.
• Mentor colleagues, share analytical techniques and best practices, and contribute to data and AI literacy across the firm.
• Stay current with data science, machine learning, AI, and analytical engineering developments and help improve tools, practices, and development standards.
• Lead by example, participate in staff mentoring and training, define team expectations, empower others, and delegate where appropriate.
• Develop or manage work plans with team members to meet project budgets, deliverables, and schedules.
• Engage in internal and external professional development opportunities.
• Contribute to sustainable strategies on project assignments and to staff knowledge development.
• Contribute to the development of standards, policies, and procedures.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior